Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. This article is a short summary of my understanding of the definition of data science in 2018. Numerous articles have been published on the meaning of data science in the past six years.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Occam's Razor

SEPTEMBER 19, 2011

You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. The team did the normal modeling to ensure that the results were statistically significant (large enough sample set, sufficient number of conversions in each variation). * ask for a raise. It is that simple. Okay, it is not simple.

Measurement 120

Measurement Advertising Testing Marketing 120

Octopai

MAY 2, 2022

Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. But the approaches and principles that form the basis of DataOps have been around for decades.

Manufacturing 98

Manufacturing Testing Data Quality Dashboards 98

FineReport

DECEMBER 19, 2019

In addition, Jupyter Notebook is also an excellent interactive tool for data analysis and provides a convenient experimental platform for beginners. Pandas incorporates a large number of analysis function methods, as well as common statistical models and visualization processing. From Google. Data Analysis Libraries.

Knowledge Discovery 98

Knowledge Discovery Visualization Data mining Reporting 98

O'Reilly on Data

MARCH 23, 2023

And it can look up an author and make statistical observations about their interests. ChatGPT offers users a paid account that costs $20/month, which is good enough for experimenters, though there is a limit on the number of requests you can make. Again, ChatGPT is predicting a response to your question.

IT 348

IT Modeling Testing Risk 348

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

DataRobot Blog

JULY 28, 2022

This article presents a case study of how DataRobot was able to achieve high accuracy and low cost by actually using techniques learned through Data Science Competitions in the process of solving a DataRobot customer’s problem. If this knock leveling could be automated, it would result in significant cost savings.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

The Unofficial Google Data Science Blog

JANUARY 16, 2018

by SANGHO YOON In this article, we discuss an approach to the design of experiments in a network. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. This simulation is based on the actual user network of GCP.

Testing 59

Testing Experimentation Measurement Modeling 59

Darkhorse

NOVEMBER 8, 2016

Santiago Ortiz’s article, 45 ways to communicate two quantiles , shows us a stunning expanse for just two numbers. Strip plots in the flesh: NPR looks at school funding The LA Times on pop music’s evolution Eurostat’s key European statistics Jittered Strip Plot. The histogram's familiarity makes it quick to interpret for many.

Visualization 71

Visualization Experimentation Measurement Statistics 71

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 45

Statistics Optimization Modeling Experimentation 45

Insight

APRIL 28, 2020

In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Article by Adam Azzam. Originally posted on Open Data Science (ODSC). Length: Highly Variable. Thanks to April Minsky, Holly Szafarek, and Geneviève Smith for feedback on drafts.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Business Over Broadway

MAY 22, 2019

Below is a list of topics, answers and articles in support of a recent Tweet Chat in which I was the guest. I have included the topics of discussion, some of my answers and a list of articles in support of the week’s topic. Also, here is an article about 1 0 digital transformation influencers that will change your world.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. As data science work is experimental and probabilistic in nature, data scientists are often faced with making inferences.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. It analyzes historical data and news articles, confirming a possible market correction. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

Cloudera

NOVEMBER 25, 2020

How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? An integrated unstructured data match engine to find similar documents, reports, articles, and patents, using natural language and/or in combination with SQL.

Data Warehouse 59

Data Warehouse Unstructured Data Analytics Visualization 59

Alation

SEPTEMBER 28, 2022

However, there is a lot more to know about DataOps, as it has its own definition, principles, benefits, and applications in real-life companies today – which we will cover in this article! Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum. What Is DataOps? Simplicity.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business. Identifying the problem.

Marketing 364

Marketing Experimentation Metrics Testing 364

Domino Data Lab

AUGUST 22, 2019

Note These three particular words are called articles , or determiners. Although it’s not perfect, [Note: These are statistical approximations, of course!] We waved our finger in the air to select 64, so some experimentation and optimization are warranted at your end if you feel like it. Example 11.6 place an LSTM() layer.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

CIO Business Intelligence

APRIL 9, 2025

One of the most fundamental tenets of statistical methods in the last century has focused on correlation to determine causation. The quantitative models that make ML-enhanced analytics possible analyze business issues through statistical, mathematical and computational techniques.

Enterprise 102

Enterprise Analytics Experimentation Statistics 102

Occam's Razor

FEBRUARY 7, 2022

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

Experimentation 103

Experimentation Testing Optimization Marketing 103

Occam's Razor

AUGUST 22, 2011

" + Strategic Analysis Articles. Tactical Analysis Articles. Blogging Experience Articles. + Book Articles. Misc Articles. In each section the listing is from the latest article to the earliest. " Strategic Analysis Articles. Tactical Analysis Articles. How do I crush tough problems?

KPI 125

KPI Metrics Measurement ROI 125

Data Science and Beyond

DECEMBER 23, 2018

The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data. Hence, the book is full of practical examples. A call for less casual causal inferences.

Deep Learning 103

Deep Learning Experimentation Data Science Data-driven 103

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Use of influence functions goes back to the 1970s in robust statistics.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Choice and Assessment of Statistical Predictions by Stone. Introduction. A Survey of Cross-Validation Procedures for Model Selection by Arlot and Celisse. Cross-Validatory.

Testing 79

Testing Modeling Machine Learning Metrics 79

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. Ultimately this will mean a more streamlined, inclusive, and personal publishing industry.

Modeling 59

Modeling Metadata Publishing Sales 59